How to Read FTIR Spectrum: Functional Groups

Fourier-Transform Infrared (FTIR) Spectroscopy, a technique used extensively in chemical analysis, yields complex spectra that, when deciphered correctly, provide valuable insights into a material’s composition. Functional groups, specific arrangements of atoms within molecules, exhibit characteristic absorption bands in the FTIR spectrum, allowing for their identification. PerkinElmer, a leading manufacturer of analytical instruments, offers resources that help to understand spectral data effectively. Mastering how to read FTIR spectrum involves understanding the relationships between these bands and the molecular structures, skills vital for researchers in labs around the world. Interpretation software, such as that provided by Thermo Fisher Scientific, assists in this complex process, helping to make sense of the data and extract meaning from the vibrational modes displayed.

Fourier Transform Infrared (FTIR) Spectroscopy stands as a cornerstone analytical technique, empowering scientists across diverse fields to identify and quantify chemical compounds with remarkable precision.

It's a go-to method for understanding the molecular composition of samples.

This technique operates by probing how molecules interact with infrared radiation. This interaction reveals crucial information about their structure and bonding.

FTIR's versatility and non-destructive nature make it indispensable in numerous applications. These range from quality control in manufacturing to cutting-edge research in material science.

What is FTIR Spectroscopy?

At its heart, FTIR spectroscopy is a technique that measures the absorption of infrared (IR) radiation by a sample.

This absorption is not random; it's highly specific to the molecules present and their unique structures. The core mechanism involves shining an IR beam through the sample and measuring which frequencies are absorbed and which pass through.

The resulting pattern of absorption creates a spectral fingerprint, a unique identifier for the substance.

FTIR finds extensive use in:

• Chemical Analysis: Identifying unknown substances, determining purity.
• Material Science: Characterizing polymers, composites, and nanomaterials.
• Environmental Monitoring: Detecting pollutants in air and water.
• Pharmaceuticals: Verifying drug identity and quality.
• Food Science: Analyzing food composition and detecting adulteration.

Compared to other spectroscopic techniques like UV-Vis or NMR, FTIR excels at identifying functional groups within molecules.

Each technique provides complementary information. UV-Vis spectroscopy focuses on electronic transitions, while NMR elucidates molecular structure through magnetic properties.

FTIR, however, offers a rapid and often simpler method for gaining insights into molecular vibrations and bonding.

The Underlying Principle: Infrared Radiation and Molecular Vibrations

The magic of FTIR lies in the interaction between infrared radiation and the molecules within a sample. Molecules are not static; their atoms are constantly vibrating in various ways.

When a molecule is exposed to IR radiation of a specific frequency that matches its natural vibrational frequency, it absorbs energy and vibrates with greater amplitude.

This absorption is detected by the FTIR spectrometer and forms the basis of the spectrum.

There's a direct relationship between a molecule's structure and the frequencies at which it vibrates. Heavier atoms and weaker bonds vibrate at lower frequencies, while lighter atoms and stronger bonds vibrate at higher frequencies.

Think of it like a guitar string: a thicker, heavier string vibrates at a lower pitch than a thin, light string.

Molecular vibrations are quantized, meaning that molecules can only vibrate at specific energy levels.

These energy levels correspond to discrete frequencies of IR radiation. The absorption of IR radiation causes a molecule to transition from a lower to a higher vibrational energy level.

Vibrational Modes

Molecules can vibrate in a variety of ways, each corresponding to a different vibrational mode. These modes are broadly classified as either stretching or bending vibrations.

• Stretching: A change in the bond length along the bond axis.

• Bending: A change in the bond angle.

Within these categories, there are several specific types of vibrations:

• Symmetric Stretching: Both atoms in a bond move in the same direction, increasing and decreasing the bond length simultaneously.

• Asymmetric Stretching: Atoms move in opposite directions, one increasing in length while the other decreases.

• Scissoring: A bending vibration where two atoms move back and forth in the same plane, like scissor blades.

• Rocking: A bending vibration where atoms move in the same direction in the same plane, maintaining a constant bond angle.

• Wagging: Atoms move out of the plane, swinging back and forth together.

• Twisting: Atoms move out of the plane, one moving up while the other moves down.

Understanding these different vibrational modes is crucial for interpreting FTIR spectra. Each mode corresponds to a specific absorption band in the spectrum.

The position, intensity, and shape of these bands provide valuable information about the molecular structure and composition of the sample.

Deciphering the FTIR Spectrum: A Guide to Wavenumbers, Transmittance, and Absorbance

Fourier Transform Infrared (FTIR) Spectroscopy stands as a cornerstone analytical technique, empowering scientists across diverse fields to identify and quantify chemical compounds with remarkable precision. It's a go-to method for understanding the molecular composition of samples. This technique operates by probing how molecules interact with infrared radiation, producing a spectrum that acts as a molecular fingerprint. Understanding how to interpret this fingerprint is key to unlocking the wealth of information FTIR offers.

Axes of the Spectrum: Wavenumber and Absorbance

The FTIR spectrum is a graphical representation of how a sample interacts with infrared light. The two axes of this graph provide the foundation for spectral interpretation.

The X-axis: Wavenumber (cm⁻¹)

The x-axis represents the wavenumber, typically measured in reciprocal centimeters (cm⁻¹). Wavenumber is directly proportional to energy and is calculated as the inverse of the wavelength.

A higher wavenumber corresponds to higher energy infrared radiation. The conventional range of wavenumbers used in FTIR spectroscopy is typically between 4000 cm⁻¹ and 400 cm⁻¹.

This range covers the fundamental vibrational frequencies of most organic and inorganic molecules.

The Y-axis: Transmittance (%T) vs. Absorbance (A)

The y-axis can be represented as either transmittance (%T) or absorbance (A). Transmittance is the percentage of infrared radiation that passes through the sample, while absorbance is the measure of the amount of infrared radiation absorbed by the sample.

Mathematically, they are related by the equation: A = -log₁₀(T), where T is transmittance expressed as a decimal (e.g., 50% transmittance = 0.5).

While both representations contain the same information, absorbance is often preferred for quantitative analysis. This preference stems from the fact that absorbance is directly proportional to the concentration of the analyte, as described by the Beer-Lambert Law.

Key Spectral Features: Peaks and Baseline

Beyond the axes, the spectrum's key features, peaks, and baseline provide direct information about the sample's composition.

Peaks/Bands: Absorption of Infrared Radiation

Peaks (also sometimes referred to as bands) in an FTIR spectrum represent the absorption of infrared radiation at specific wavenumbers. When a molecule absorbs infrared radiation, it transitions to a higher vibrational energy level. The wavenumber at which a peak appears corresponds to the frequency of this vibration.

Peak shape is influenced by several factors, including the nature of the chemical bond, the surrounding molecular environment, and instrumental resolution. Broadening can occur due to hydrogen bonding or unresolved vibrational modes. Intensity is related to the concentration of the absorbing species and the strength of the interaction between the molecule and the infrared radiation.

Baseline: A Reference Point

The baseline represents the background level of infrared radiation detected by the instrument in the absence of significant sample absorption. Ideally, the baseline should be flat and horizontal.

However, in practice, deviations from this ideal can occur due to factors such as scattering effects, variations in the refractive index of the sample, or instrumental artifacts. Baseline correction is crucial for accurate data analysis, especially for quantitative measurements.

Common causes of baseline drift include:

• Scattering from particulate matter in the sample.
• Changes in the refractive index of the sample with wavenumber.
• Instrumental drift or instability.

Functional Groups and Characteristic Absorption: Identifying Molecular Components

The power of FTIR lies in its ability to identify specific functional groups within a molecule based on their characteristic absorption patterns.

Relating Functional Groups to Peak Positions

Certain functional groups (e.g., C=O, O-H, N-H, C-H) absorb infrared radiation at characteristic wavenumbers. By examining the positions of peaks in the FTIR spectrum, it is possible to identify the presence of these functional groups in the sample.

For example, a strong absorption band around 1700 cm⁻¹ typically indicates the presence of a carbonyl group (C=O). A broad absorption band between 3200 cm⁻¹ and 3600 cm⁻¹ usually suggests the presence of an alcohol (O-H) or amine (N-H) group. The C-H stretching frequencies generally appear in the region of 2850-3000 cm⁻¹.

The following table shows some functional group absorptions with their approximate wavenumber ranges and typical intensities.

Group Frequencies and Environmental Influences

The concept of group frequencies refers to the fact that certain functional groups tend to absorb infrared radiation within relatively narrow wavenumber ranges. However, the exact position of these absorptions can be affected by the neighboring atoms or groups within the molecule.

Factors such as inductive effects, resonance, and steric hindrance can all influence the vibrational frequencies of functional groups. These subtle shifts in peak positions can provide valuable information about the molecular environment surrounding the functional group.

Bond Strength, Atomic Mass, and Vibrational Frequency

The vibrational frequency of a bond is related to its strength and the masses of the atoms involved. Stronger bonds vibrate at higher frequencies (higher wavenumbers), while heavier atoms vibrate at lower frequencies (lower wavenumbers). This relationship can be understood through the following equation, which is derived from Hooke's Law:

v = (1/2πc) * √(k/µ)

Where:

• v is the vibrational frequency (wavenumber)
• c is the speed of light
• k is the force constant (bond strength)
• µ is the reduced mass (related to the atomic masses of the atoms involved in the bond)

The Fingerprint Region: Molecular Individuality

The region of the FTIR spectrum below approximately 1500 cm⁻¹ is often referred to as the fingerprint region.

Uniqueness of the Fingerprint Region

This region is characterized by a complex pattern of overlapping absorptions due to skeletal vibrations and other complex vibrational modes.

Compound Identification Using the Fingerprint Region

Although it can be difficult to assign specific peaks in the fingerprint region to particular functional groups, the overall pattern is highly specific to each molecule.

This makes the fingerprint region invaluable for compound identification. By comparing the fingerprint region of an unknown spectrum to reference spectra, it is often possible to identify the compound with a high degree of certainty. Think of it like comparing fingerprints – even though you can't describe every ridge and valley, the overall pattern is unique to each individual.

Navigating Spectral Variations: Factors Influencing the FTIR Spectrum

Having explored the fundamentals of FTIR spectroscopy and the interpretation of spectral data, it's crucial to acknowledge that several factors can influence the appearance of an FTIR spectrum. Understanding these variables is key to accurate spectral interpretation and reliable analysis. This section will delve into the instrumental parameters, chemical environment, and advanced phenomena that can affect your results.

Instrumental Parameters and Spectral Quality

The parameters set on the FTIR instrument itself can have a significant impact on the resulting spectrum. Two key parameters to consider are resolution and aperture size.

Resolution: Separating the Wheat from the Chaff

Resolution refers to the instrument's ability to distinguish between closely spaced peaks. Higher resolution allows for better separation of overlapping peaks, providing more detailed spectral information.

However, increasing resolution often comes at the cost of a reduced signal-to-noise ratio. This means that the spectral features may become noisier, potentially obscuring weaker peaks. A balance must therefore be struck between resolution and signal quality, depending on the specific application and sample characteristics.

The optimal resolution depends on the complexity of the sample and the information you need to extract. For simple compounds with well-separated peaks, a lower resolution may suffice.

However, for complex mixtures or samples with closely overlapping peaks, a higher resolution is necessary.

Aperture Size: Light Throughput and Sensitivity

The aperture size controls the amount of infrared radiation that passes through the sample. A larger aperture allows more light to reach the detector, potentially increasing the signal-to-noise ratio.

However, a larger aperture can also lead to a decrease in resolution, as the beam of light becomes less collimated.

Conversely, a smaller aperture improves resolution but reduces light throughput, potentially decreasing the signal-to-noise ratio.

Choosing the appropriate aperture size involves a trade-off between sensitivity and resolution.

Chemical and Physical Influences on Vibrational Modes

The chemical environment surrounding a molecule can significantly alter its vibrational modes and, consequently, its FTIR spectrum.

Hydrogen Bonding: A Broadening Effect

Hydrogen bonding profoundly impacts the vibrational frequencies of O-H and N-H groups. The formation of hydrogen bonds weakens the O-H or N-H bond, causing the corresponding stretching frequencies to shift to lower wavenumbers.

Furthermore, hydrogen bonding leads to broadening of the peaks due to the distribution of hydrogen bond strengths and geometries. This broadening effect can sometimes obscure fine spectral details.

Solvent Effects: A Subtle Shift

The solvent used for sample preparation can also influence the spectrum. Solvent-solute interactions can lead to shifts in peak positions and changes in peak intensities.

Polar solvents, in particular, can interact strongly with polar functional groups in the sample, affecting their vibrational frequencies. It's important to choose a solvent that does not interfere with the spectral region of interest.

Concentration Effects: Beer-Lambert Law

The concentration of the analyte directly affects the intensity of the absorption bands, as described by the Beer-Lambert Law.

Higher concentrations result in stronger absorption bands, while lower concentrations lead to weaker bands. This relationship can be utilized for quantitative analysis, but it's crucial to ensure that the Beer-Lambert Law holds true for the specific system under investigation.

Phase: Solid, Liquid, or Gas?

The physical state of the sample (solid, liquid, or gas) can also influence the FTIR spectrum.

In solids, intermolecular interactions are typically stronger, leading to broader peaks compared to liquids or gases.

Gases often exhibit sharp, well-defined peaks due to the minimal intermolecular interactions.

The choice of sample preparation technique and the interpretation of the spectrum should take into account the phase of the sample.

Advanced Phenomena: Unveiling Spectral Nuances

Beyond the fundamental absorptions arising from specific functional groups, certain advanced phenomena can manifest in FTIR spectra, adding complexity but also providing valuable insights.

Harmonic Overtones: Multiples of the Fundamental

Harmonic overtones occur at frequencies that are approximately integer multiples of the fundamental vibrational frequencies. These overtones arise from deviations from ideal harmonic behavior in molecular vibrations.

Overtones are typically much weaker in intensity than the fundamental absorptions. Identifying overtones can sometimes aid in the assignment of fundamental vibrations, especially in complex spectra.

Fermi Resonance: Peak Splitting Explained

Fermi resonance is a phenomenon where a fundamental vibration interacts with an overtone or combination band of similar energy and symmetry. This interaction leads to a splitting of the peaks, resulting in two peaks instead of one.

The observation of Fermi resonance can provide information about the vibrational coupling between different modes within the molecule.

Combination Bands: Sums and Differences

Combination bands arise from the simultaneous excitation of two or more vibrational modes. The frequency of a combination band is approximately equal to the sum or difference of the frequencies of the individual vibrations.

Combination bands are generally weaker in intensity than fundamental absorptions, but they can provide additional information about the vibrational modes of the molecule.

From Sample to Spectrum: Practical Aspects of FTIR Spectroscopy

Having explored the fundamentals of FTIR spectroscopy and the interpretation of spectral data, it's time to delve into the practical steps involved in obtaining a high-quality spectrum. This involves selecting the appropriate sampling technique, preparing the sample correctly, and then analyzing the resulting data using spectral databases and software tools. Mastering these practical aspects is crucial for obtaining meaningful and reliable results from FTIR spectroscopy.

Sample Preparation: Laying the Groundwork for Accurate Analysis

The quality of your FTIR spectrum hinges significantly on proper sample preparation. The right method depends on the physical state of your sample (solid, liquid, gas) and its chemical properties. Let's explore some common sampling techniques.

Sampling Techniques: A Comparative Overview

FTIR spectroscopy offers a versatile range of sampling techniques, each suited for different types of samples. Understanding the strengths and limitations of each method is essential for choosing the optimal approach.

Transmission Spectroscopy:

In transmission spectroscopy, the IR beam passes directly through the sample. This is a straightforward technique, but it requires the sample to be sufficiently transparent to infrared radiation.

• KBr Pellets: For solid samples, a common approach involves grinding the sample with potassium bromide (KBr) powder and pressing the mixture into a transparent pellet. KBr is transparent in the mid-IR region, making it an ideal matrix material. This method is suitable for a wide range of solid samples, but it requires careful control of particle size and pellet thickness to minimize scattering effects.

• Thin Films: Thin films can be analyzed directly if they are sufficiently thin and transparent. This method is often used for polymer films or coatings. The film thickness should be optimized to obtain a good signal without excessive absorbance.

Reflectance Spectroscopy:

In reflectance spectroscopy, the IR beam is reflected off the sample surface. This is a useful technique for samples that are difficult to prepare as thin films or KBr pellets.

• ATR (Attenuated Total Reflectance): ATR is a surface-sensitive technique where the IR beam is directed onto a crystal with a high refractive index. The beam undergoes total internal reflection within the crystal, creating an evanescent wave that penetrates a short distance into the sample in contact with the crystal. This method is particularly well-suited for analyzing liquids, pastes, and powders without extensive sample preparation. Different ATR crystal materials (e.g., diamond, germanium, zinc selenide) are available, each with its own spectral range and chemical compatibility.

• Diffuse Reflectance: Diffuse reflectance is used for analyzing powders and rough surfaces. The IR beam is scattered by the sample, and the diffusely reflected light is collected by the detector. This method requires careful calibration to correct for scattering effects.

Gas Cells:

For gas samples, a gas cell is used to contain the sample in the path of the IR beam. The cell path length should be chosen to optimize the signal intensity. Longer path lengths are needed for low-concentration gases.

ATR in Detail: A Workhorse Technique

Attenuated Total Reflectance (ATR) has become a widely used sampling technique in FTIR spectroscopy due to its simplicity and versatility. It requires minimal sample preparation and can be used for a wide range of materials.

The technique relies on the principle of total internal reflection. When an IR beam enters an ATR crystal at an angle greater than the critical angle, it undergoes total internal reflection.

An evanescent wave is created at the crystal surface that penetrates a short distance (typically 1-5 micrometers) into the sample in contact with the crystal. This evanescent wave interacts with the sample, and the absorbed radiation is measured by the detector.

ATR offers several advantages, including:

• Minimal sample preparation
• High sensitivity for surface analysis
• Ability to analyze liquids, pastes, and powders directly
• Relatively insensitive to particle size effects

Data Analysis and Interpretation: Extracting Meaning from the Spectrum

Once you've obtained an FTIR spectrum, the next step is to analyze the data and extract meaningful information. This involves using spectral databases for compound identification and employing software tools for spectral processing and analysis.

Leveraging Spectral Databases for Compound Identification

Spectral databases are invaluable resources for identifying unknown compounds by comparing their FTIR spectra to reference spectra. Databases such as the NIST library and commercial databases contain thousands of spectra of known compounds.

The process involves searching the database for spectra that closely match the unknown spectrum. A good spectral match is indicated by a high correlation coefficient between the unknown and reference spectra.

It's important to note that spectral matching is not always straightforward. Factors such as sample purity, instrumental resolution, and spectral artifacts can affect the accuracy of the match.

Software Tools: Enhancing and Analyzing Your Data

Specialized software packages are essential for processing and analyzing FTIR spectra. These tools provide a range of functions, including:

• Baseline Correction: Corrects for baseline drift caused by scattering or instrumental artifacts.

• Peak Picking: Identifies and labels peaks in the spectrum, providing information on their position (wavenumber) and intensity.

• Spectral Subtraction: Removes the contribution of known compounds from the spectrum to reveal the spectrum of the unknown component.

• Library Searching: Automates the process of searching spectral databases for matching spectra.

• Quantitation: Determines the concentration of specific components in the sample based on peak intensities and calibration curves.

Quantitative Analysis: Applying Beer-Lambert Law

FTIR spectroscopy can be used for quantitative analysis, which involves determining the concentration of specific components in a sample. The basis for quantitative analysis in FTIR is the Beer-Lambert law:

A = εbc

Where:

• A is the absorbance
• ε is the molar absorptivity (a constant for a given substance at a given wavenumber)
• b is the path length of the IR beam through the sample
• c is the concentration

By measuring the absorbance of a sample at a specific wavenumber and knowing the molar absorptivity and path length, the concentration can be determined. Accurate quantitative analysis requires careful calibration using standards of known concentration.

Inside the FTIR Spectrometer: Understanding the Instrumentation and Software

From Sample to Spectrum: Practical Aspects of FTIR Spectroscopy Having explored the fundamentals of FTIR spectroscopy and the interpretation of spectral data, it's time to delve into the practical steps involved in obtaining a high-quality spectrum. This involves selecting the appropriate sampling technique, preparing the sample correctly, and then understanding the instrumentation involved. We now turn our attention to the heart of the FTIR process: the spectrometer itself, and the software that brings the data to life. This section provides a comprehensive overview of the key components within an FTIR spectrometer and the essential software functionalities that enable accurate spectral analysis.

Components of FTIR Spectrometers

The FTIR spectrometer is a sophisticated instrument comprising several key components that work in harmony to generate and analyze infrared spectra. Understanding the function of each component is crucial for optimizing instrument performance and ensuring accurate data acquisition.

Infrared Source

The infrared source is the heart of the FTIR spectrometer, responsible for emitting the infrared radiation that interacts with the sample. Several types of IR sources are commonly used:

• Globar sources are heated ceramic rods that emit broadband infrared radiation. They are robust, relatively inexpensive, and suitable for many applications. Globar sources are particularly effective for mid-IR measurements.

• Mercury-cadmium-telluride (MCT) sources offer higher intensity and are often preferred for applications requiring high sensitivity. These sources are particularly beneficial for low-signal measurements or for analyzing small samples.

The selection of the appropriate source depends on the spectral range and the sensitivity requirements of the experiment.

Interferometer

The interferometer is the core of the FTIR spectrometer, responsible for generating the interferogram, which contains all the spectral information. The most common type of interferometer is the Michelson interferometer.

The Michelson interferometer works by splitting the infrared beam into two paths:

• One path is reflected by a fixed mirror.

• The other path is reflected by a moving mirror.

The two beams are then recombined, creating interference patterns. The movement of the mirror creates a varying path difference, resulting in constructive and destructive interference.

This interference pattern, the interferogram, is a time-domain representation of the infrared radiation. A Fourier transform is then applied to the interferogram to convert it into the familiar frequency-domain spectrum. The accuracy and precision of the mirror movement are critical for obtaining high-quality spectra.

Detector

The detector plays a crucial role in converting the infrared signal that has interacted with the sample into an electrical signal that can be processed. There are two main types of IR detectors:

• Deuterated Triglycine Sulfate (DTGS) detectors are thermal detectors that measure the change in temperature caused by the absorption of infrared radiation. DTGS detectors are relatively inexpensive and suitable for a wide range of applications.

• Mercury Cadmium Telluride (MCT) detectors are photon detectors that are more sensitive than DTGS detectors. MCT detectors are cryogenically cooled to reduce thermal noise, enhancing their sensitivity. They are particularly useful for applications requiring high sensitivity or for measuring weak signals.

The choice of detector depends on the sensitivity requirements and the spectral range of the experiment.

Sample Compartment

The sample compartment is the area where the sample is placed for analysis. It's designed to accommodate various sampling accessories, such as transmission holders, ATR (Attenuated Total Reflectance) crystals, and reflectance attachments.

The sample compartment must be light-tight and shielded from external interference to ensure accurate measurements.

The design of the sample compartment often allows for easy access and quick changes of sampling accessories.

Analog-to-Digital Converter (ADC)

The analog-to-digital converter (ADC) is responsible for converting the analog electrical signal from the detector into a digital signal that can be processed by the computer.

The ADC samples the analog signal at regular intervals and assigns a digital value to each sample. The resolution of the ADC, measured in bits, determines the accuracy of the digital representation of the analog signal.

A higher resolution ADC provides a more accurate representation of the signal and improves the overall quality of the spectrum.

Software for Spectral Analysis: Key Features

FTIR software packages provide a range of tools for processing, analyzing, and interpreting infrared spectra. These software packages enable researchers to extract valuable information from their data, including compound identification and quantitative analysis.

Baseline Correction

Baseline correction is a crucial step in spectral processing, as it removes the effects of baseline drift and curvature, which can arise from various factors such as scattering and instrumental artifacts.

Baseline correction algorithms work by estimating the baseline and subtracting it from the spectrum. Different algorithms are available, including polynomial fitting and rubber band correction.

Proper baseline correction is essential for accurate peak identification and quantitative analysis.

Peak Picking

Peak picking is the process of automatically identifying and labeling peaks in the spectrum. Peak-picking algorithms typically identify peaks based on their height, width, and shape.

The software automatically locates the peaks in the spectrum and provides their wavenumbers and intensities. Manual adjustment of peak picking parameters may be necessary to ensure accurate results.

Library Searching

Library searching is a powerful tool for identifying unknown compounds by comparing their spectra to reference spectra in a spectral library.

The software compares the unknown spectrum to the reference spectra and calculates a similarity index for each match. The software then returns a list of the most likely matches, along with their similarity indices.

Several spectral libraries are available, including commercial databases and public domain libraries.

Spectral Manipulation

FTIR software packages offer a range of tools for spectral manipulation, including spectral subtraction and smoothing.

• Spectral subtraction involves subtracting the spectrum of one component from the spectrum of a mixture to isolate the spectrum of the remaining components. This is useful for identifying components in complex mixtures.

• Smoothing is a technique used to reduce noise in the spectrum. Smoothing algorithms typically involve averaging data points over a small spectral range.

Quantitation

FTIR software packages can perform quantitative analysis based on Beer-Lambert law, which states that the absorbance of a substance is directly proportional to its concentration and path length.

The software calculates the concentration of the analyte by measuring the absorbance at a specific wavenumber and applying the Beer-Lambert law. Accurate quantitative analysis requires careful calibration and validation.

So, that's the gist of identifying functional groups in an FTIR spectrum. Don't be intimidated by all those squiggly lines! With a little practice and this guide on how to read FTIR spectrum, you'll be spotting alcohols, carbonyls, and more like a pro in no time. Now go forth and analyze!